Using Machine Learning in communication network research

Abstract

International audienceNowadays, Machine Learning (ML) tools are commonly used in every area of science or technology. Networking is not an exception, and we find ML all over the research activities in most fields composing the domain. In this talk, we will briefly describe a set of research activities we have developed along several years around several pretty different families of problems, using ML methods. They concern (i) the automatic and accurate real time measure of the Quality of Experience of an application or service built on top of the Internet around the transport of video or audio content (e.g. video streaming, IP telephony, video-conferencing, etc.), (ii) network tomography (measuring on the edges to infer values inside the network), (iii) time series forecasting in several contexts, in particular concept drift detection or anomalies detection, and (iv) service placements in Software Defined Networks, a central problem in 5G and B5G technologies. The corresponding ML tools are mainly Supervised Learning and Reinforcement Learning, even if we are currently using Unsupervised Learning in recent activities of point (i). After this global presentation we will make one or two zooms on some specific results we obtained with these powerful tools, and some of the current projects we are currently developing